The committee's decision to single out this article was explained as
follows: The article contributes an innovative approach to recommender
systems. Its main contribution is an extension of two existing methods on
integrating heterogeneous data (user feedback) in a collaborative
recommendation system. Using transfer learning, the authors' extension
addresses the issue of uncertainty. This process is iterative: (1) maps
learned recommendation (built using labeled data) to unlabeled data, and
(2) identifies items that are likely going to be highly reviewed to get
further input. Because recommender systems are an important topic in HCI,
and this paper introduces a sensible and broadly applicable technique to a
wide class of interactive machine learning systems, it makes an important
contribution to the field of intelligent interactive systems.